R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(21,2472.81,19,2407.6,25,2454.62,21,2448.05,23,2497.84,23,2645.64,19,2756.76,18,2849.27,19,2921.44,19,2981.85,22,3080.58,23,3106.22,20,3119.31,14,3061.26,14,3097.31,14,3161.69,15,3257.16,11,3277.01,17,3295.32,16,3363.99,20,3494.17,24,3667.03,23,3813.06,20,3917.96,21,3895.51,19,3801.06,23,3570.12,23,3701.61,23,3862.27,23,3970.1,27,4138.52,26,4199.75,17,4290.89,24,4443.91,26,4502.64,24,4356.98,27,4591.27,27,4696.96,26,4621.4,24,4562.84,23,4202.52,23,4296.49,24,4435.23,17,4105.18,21,4116.68,19,3844.49,22,3720.98,22,3674.4,18,3857.62,16,3801.06,14,3504.37,12,3032.6,14,3047.03,16,2962.34,8,2197.82,3,2014.45,0,1862.83,5,1905.41,1,1810.99,1,1670.07,3,1864.44),dim=c(2,61),dimnames=list(c('Consvertr','Aand'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Consvertr','Aand'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Consvertr Aand M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 21 2472.81 1 0 0 0 0 0 0 0 0 0 0
2 19 2407.60 0 1 0 0 0 0 0 0 0 0 0
3 25 2454.62 0 0 1 0 0 0 0 0 0 0 0
4 21 2448.05 0 0 0 1 0 0 0 0 0 0 0
5 23 2497.84 0 0 0 0 1 0 0 0 0 0 0
6 23 2645.64 0 0 0 0 0 1 0 0 0 0 0
7 19 2756.76 0 0 0 0 0 0 1 0 0 0 0
8 18 2849.27 0 0 0 0 0 0 0 1 0 0 0
9 19 2921.44 0 0 0 0 0 0 0 0 1 0 0
10 19 2981.85 0 0 0 0 0 0 0 0 0 1 0
11 22 3080.58 0 0 0 0 0 0 0 0 0 0 1
12 23 3106.22 0 0 0 0 0 0 0 0 0 0 0
13 20 3119.31 1 0 0 0 0 0 0 0 0 0 0
14 14 3061.26 0 1 0 0 0 0 0 0 0 0 0
15 14 3097.31 0 0 1 0 0 0 0 0 0 0 0
16 14 3161.69 0 0 0 1 0 0 0 0 0 0 0
17 15 3257.16 0 0 0 0 1 0 0 0 0 0 0
18 11 3277.01 0 0 0 0 0 1 0 0 0 0 0
19 17 3295.32 0 0 0 0 0 0 1 0 0 0 0
20 16 3363.99 0 0 0 0 0 0 0 1 0 0 0
21 20 3494.17 0 0 0 0 0 0 0 0 1 0 0
22 24 3667.03 0 0 0 0 0 0 0 0 0 1 0
23 23 3813.06 0 0 0 0 0 0 0 0 0 0 1
24 20 3917.96 0 0 0 0 0 0 0 0 0 0 0
25 21 3895.51 1 0 0 0 0 0 0 0 0 0 0
26 19 3801.06 0 1 0 0 0 0 0 0 0 0 0
27 23 3570.12 0 0 1 0 0 0 0 0 0 0 0
28 23 3701.61 0 0 0 1 0 0 0 0 0 0 0
29 23 3862.27 0 0 0 0 1 0 0 0 0 0 0
30 23 3970.10 0 0 0 0 0 1 0 0 0 0 0
31 27 4138.52 0 0 0 0 0 0 1 0 0 0 0
32 26 4199.75 0 0 0 0 0 0 0 1 0 0 0
33 17 4290.89 0 0 0 0 0 0 0 0 1 0 0
34 24 4443.91 0 0 0 0 0 0 0 0 0 1 0
35 26 4502.64 0 0 0 0 0 0 0 0 0 0 1
36 24 4356.98 0 0 0 0 0 0 0 0 0 0 0
37 27 4591.27 1 0 0 0 0 0 0 0 0 0 0
38 27 4696.96 0 1 0 0 0 0 0 0 0 0 0
39 26 4621.40 0 0 1 0 0 0 0 0 0 0 0
40 24 4562.84 0 0 0 1 0 0 0 0 0 0 0
41 23 4202.52 0 0 0 0 1 0 0 0 0 0 0
42 23 4296.49 0 0 0 0 0 1 0 0 0 0 0
43 24 4435.23 0 0 0 0 0 0 1 0 0 0 0
44 17 4105.18 0 0 0 0 0 0 0 1 0 0 0
45 21 4116.68 0 0 0 0 0 0 0 0 1 0 0
46 19 3844.49 0 0 0 0 0 0 0 0 0 1 0
47 22 3720.98 0 0 0 0 0 0 0 0 0 0 1
48 22 3674.40 0 0 0 0 0 0 0 0 0 0 0
49 18 3857.62 1 0 0 0 0 0 0 0 0 0 0
50 16 3801.06 0 1 0 0 0 0 0 0 0 0 0
51 14 3504.37 0 0 1 0 0 0 0 0 0 0 0
52 12 3032.60 0 0 0 1 0 0 0 0 0 0 0
53 14 3047.03 0 0 0 0 1 0 0 0 0 0 0
54 16 2962.34 0 0 0 0 0 1 0 0 0 0 0
55 8 2197.82 0 0 0 0 0 0 1 0 0 0 0
56 3 2014.45 0 0 0 0 0 0 0 1 0 0 0
57 0 1862.83 0 0 0 0 0 0 0 0 1 0 0
58 5 1905.41 0 0 0 0 0 0 0 0 0 1 0
59 1 1810.99 0 0 0 0 0 0 0 0 0 0 1
60 1 1670.07 0 0 0 0 0 0 0 0 0 0 0
61 3 1864.44 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Aand M1 M2 M3 M4
-2.546239 0.006142 0.609521 -0.280403 1.758527 0.577458
M5 M6 M7 M8 M9 M10
1.426558 0.676751 0.879589 -1.762926 -2.551330 0.056200
M11
0.551096
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.1282 -3.7573 -0.3529 3.1751 10.7111
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.5462390 3.4796775 -0.732 0.468
Aand 0.0061421 0.0007954 7.722 5.81e-10 ***
M1 0.6095209 3.0365131 0.201 0.842
M2 -0.2804032 3.1756463 -0.088 0.930
M3 1.7585270 3.1724023 0.554 0.582
M4 0.5774579 3.1714454 0.182 0.856
M5 1.4265582 3.1713940 0.450 0.655
M6 0.6767509 3.1720383 0.213 0.832
M7 0.8795894 3.1713528 0.277 0.783
M8 -1.7629256 3.1714631 -0.556 0.581
M9 -2.5513297 3.1713208 -0.805 0.425
M10 0.0562002 3.1713692 0.018 0.986
M11 0.5510958 3.1714783 0.174 0.863
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.014 on 48 degrees of freedom
Multiple R-squared: 0.5715, Adjusted R-squared: 0.4644
F-statistic: 5.335 on 12 and 48 DF, p-value: 1.242e-05
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.6038017 0.792396683 0.396198341
[2,] 0.4400812 0.880162377 0.559918812
[3,] 0.5837742 0.832451522 0.416225761
[4,] 0.5329312 0.934137569 0.467068784
[5,] 0.4651765 0.930352952 0.534823524
[6,] 0.6510357 0.697928661 0.348964331
[7,] 0.9141561 0.171687795 0.085843898
[8,] 0.9189126 0.162174729 0.081087365
[9,] 0.8797356 0.240528803 0.120264402
[10,] 0.8901270 0.219746036 0.109873018
[11,] 0.9018919 0.196216153 0.098108076
[12,] 0.9559274 0.088145147 0.044072574
[13,] 0.9829878 0.034024366 0.017012183
[14,] 0.9812708 0.037458382 0.018729191
[15,] 0.9791283 0.041743323 0.020871661
[16,] 0.9888570 0.022286049 0.011143024
[17,] 0.9976648 0.004670346 0.002335173
[18,] 0.9982718 0.003456328 0.001728164
[19,] 0.9963383 0.007323408 0.003661704
[20,] 0.9922436 0.015512854 0.007756427
[21,] 0.9860806 0.027838743 0.013919371
[22,] 0.9786250 0.042749933 0.021374967
[23,] 0.9779911 0.044017750 0.022008875
[24,] 0.9661984 0.067603193 0.033801596
[25,] 0.9349864 0.130027181 0.065013590
[26,] 0.8818388 0.236322406 0.118161203
[27,] 0.8650825 0.269835075 0.134917537
[28,] 0.8140252 0.371949683 0.185974841
[29,] 0.8248188 0.350362415 0.175181207
[30,] 0.6770847 0.645830598 0.322915299
> postscript(file="/var/www/html/rcomp/tmp/103lw1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ulic1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3g2y51258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/488tg1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5oobi1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6 7
7.7483679 7.0388211 10.7110874 7.9325103 8.7775928 8.6195914 3.7342381
8 9 10 11 12 13 14
4.8085436 6.1536693 3.1750926 5.0737833 6.4673946 2.7774731 -1.9760515
15 16 17 18 19 20 21
-4.2364059 -3.4507679 -3.8862585 -7.2583728 -1.5736739 -0.3529398 3.6358802
22 23 24 25 26 27 28
3.9666197 1.5747871 -1.5184278 -0.9900576 -1.5200082 1.8595279 2.2329667
29 30 31 32 33 34 35
0.3970699 0.4845700 3.2472719 4.5137036 -4.2576872 -0.8050876 0.3392888
36 37 38 39 40 41 42
-0.2149510 0.7364857 0.9772468 -1.5975832 -2.0568303 -1.6927939 -1.5201637
43 44 45 46 47 48 49
-1.5751631 -3.9054340 0.8123354 -2.1233648 1.1403555 1.9775523 -3.7573318
50 51 52 53 54 55 56
-4.5200082 -6.7366262 -4.6578788 -3.5956102 -0.3256249 -3.8326730 -5.0638734
57 58 59 60 61
-6.3441978 -4.2132600 -8.1282146 -6.7115681 -6.5149372
> postscript(file="/var/www/html/rcomp/tmp/6xvap1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 7.7483679 NA
1 7.0388211 7.7483679
2 10.7110874 7.0388211
3 7.9325103 10.7110874
4 8.7775928 7.9325103
5 8.6195914 8.7775928
6 3.7342381 8.6195914
7 4.8085436 3.7342381
8 6.1536693 4.8085436
9 3.1750926 6.1536693
10 5.0737833 3.1750926
11 6.4673946 5.0737833
12 2.7774731 6.4673946
13 -1.9760515 2.7774731
14 -4.2364059 -1.9760515
15 -3.4507679 -4.2364059
16 -3.8862585 -3.4507679
17 -7.2583728 -3.8862585
18 -1.5736739 -7.2583728
19 -0.3529398 -1.5736739
20 3.6358802 -0.3529398
21 3.9666197 3.6358802
22 1.5747871 3.9666197
23 -1.5184278 1.5747871
24 -0.9900576 -1.5184278
25 -1.5200082 -0.9900576
26 1.8595279 -1.5200082
27 2.2329667 1.8595279
28 0.3970699 2.2329667
29 0.4845700 0.3970699
30 3.2472719 0.4845700
31 4.5137036 3.2472719
32 -4.2576872 4.5137036
33 -0.8050876 -4.2576872
34 0.3392888 -0.8050876
35 -0.2149510 0.3392888
36 0.7364857 -0.2149510
37 0.9772468 0.7364857
38 -1.5975832 0.9772468
39 -2.0568303 -1.5975832
40 -1.6927939 -2.0568303
41 -1.5201637 -1.6927939
42 -1.5751631 -1.5201637
43 -3.9054340 -1.5751631
44 0.8123354 -3.9054340
45 -2.1233648 0.8123354
46 1.1403555 -2.1233648
47 1.9775523 1.1403555
48 -3.7573318 1.9775523
49 -4.5200082 -3.7573318
50 -6.7366262 -4.5200082
51 -4.6578788 -6.7366262
52 -3.5956102 -4.6578788
53 -0.3256249 -3.5956102
54 -3.8326730 -0.3256249
55 -5.0638734 -3.8326730
56 -6.3441978 -5.0638734
57 -4.2132600 -6.3441978
58 -8.1282146 -4.2132600
59 -6.7115681 -8.1282146
60 -6.5149372 -6.7115681
61 NA -6.5149372
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.0388211 7.7483679
[2,] 10.7110874 7.0388211
[3,] 7.9325103 10.7110874
[4,] 8.7775928 7.9325103
[5,] 8.6195914 8.7775928
[6,] 3.7342381 8.6195914
[7,] 4.8085436 3.7342381
[8,] 6.1536693 4.8085436
[9,] 3.1750926 6.1536693
[10,] 5.0737833 3.1750926
[11,] 6.4673946 5.0737833
[12,] 2.7774731 6.4673946
[13,] -1.9760515 2.7774731
[14,] -4.2364059 -1.9760515
[15,] -3.4507679 -4.2364059
[16,] -3.8862585 -3.4507679
[17,] -7.2583728 -3.8862585
[18,] -1.5736739 -7.2583728
[19,] -0.3529398 -1.5736739
[20,] 3.6358802 -0.3529398
[21,] 3.9666197 3.6358802
[22,] 1.5747871 3.9666197
[23,] -1.5184278 1.5747871
[24,] -0.9900576 -1.5184278
[25,] -1.5200082 -0.9900576
[26,] 1.8595279 -1.5200082
[27,] 2.2329667 1.8595279
[28,] 0.3970699 2.2329667
[29,] 0.4845700 0.3970699
[30,] 3.2472719 0.4845700
[31,] 4.5137036 3.2472719
[32,] -4.2576872 4.5137036
[33,] -0.8050876 -4.2576872
[34,] 0.3392888 -0.8050876
[35,] -0.2149510 0.3392888
[36,] 0.7364857 -0.2149510
[37,] 0.9772468 0.7364857
[38,] -1.5975832 0.9772468
[39,] -2.0568303 -1.5975832
[40,] -1.6927939 -2.0568303
[41,] -1.5201637 -1.6927939
[42,] -1.5751631 -1.5201637
[43,] -3.9054340 -1.5751631
[44,] 0.8123354 -3.9054340
[45,] -2.1233648 0.8123354
[46,] 1.1403555 -2.1233648
[47,] 1.9775523 1.1403555
[48,] -3.7573318 1.9775523
[49,] -4.5200082 -3.7573318
[50,] -6.7366262 -4.5200082
[51,] -4.6578788 -6.7366262
[52,] -3.5956102 -4.6578788
[53,] -0.3256249 -3.5956102
[54,] -3.8326730 -0.3256249
[55,] -5.0638734 -3.8326730
[56,] -6.3441978 -5.0638734
[57,] -4.2132600 -6.3441978
[58,] -8.1282146 -4.2132600
[59,] -6.7115681 -8.1282146
[60,] -6.5149372 -6.7115681
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.0388211 7.7483679
2 10.7110874 7.0388211
3 7.9325103 10.7110874
4 8.7775928 7.9325103
5 8.6195914 8.7775928
6 3.7342381 8.6195914
7 4.8085436 3.7342381
8 6.1536693 4.8085436
9 3.1750926 6.1536693
10 5.0737833 3.1750926
11 6.4673946 5.0737833
12 2.7774731 6.4673946
13 -1.9760515 2.7774731
14 -4.2364059 -1.9760515
15 -3.4507679 -4.2364059
16 -3.8862585 -3.4507679
17 -7.2583728 -3.8862585
18 -1.5736739 -7.2583728
19 -0.3529398 -1.5736739
20 3.6358802 -0.3529398
21 3.9666197 3.6358802
22 1.5747871 3.9666197
23 -1.5184278 1.5747871
24 -0.9900576 -1.5184278
25 -1.5200082 -0.9900576
26 1.8595279 -1.5200082
27 2.2329667 1.8595279
28 0.3970699 2.2329667
29 0.4845700 0.3970699
30 3.2472719 0.4845700
31 4.5137036 3.2472719
32 -4.2576872 4.5137036
33 -0.8050876 -4.2576872
34 0.3392888 -0.8050876
35 -0.2149510 0.3392888
36 0.7364857 -0.2149510
37 0.9772468 0.7364857
38 -1.5975832 0.9772468
39 -2.0568303 -1.5975832
40 -1.6927939 -2.0568303
41 -1.5201637 -1.6927939
42 -1.5751631 -1.5201637
43 -3.9054340 -1.5751631
44 0.8123354 -3.9054340
45 -2.1233648 0.8123354
46 1.1403555 -2.1233648
47 1.9775523 1.1403555
48 -3.7573318 1.9775523
49 -4.5200082 -3.7573318
50 -6.7366262 -4.5200082
51 -4.6578788 -6.7366262
52 -3.5956102 -4.6578788
53 -0.3256249 -3.5956102
54 -3.8326730 -0.3256249
55 -5.0638734 -3.8326730
56 -6.3441978 -5.0638734
57 -4.2132600 -6.3441978
58 -8.1282146 -4.2132600
59 -6.7115681 -8.1282146
60 -6.5149372 -6.7115681
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/709fw1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xd1e1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qxiy1258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10h6001258616066.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11eazu1258616066.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12fvxq1258616067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13k1sa1258616067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14qqj31258616067.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15jgej1258616067.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1675mx1258616067.tab")
+ }
>
> system("convert tmp/103lw1258616066.ps tmp/103lw1258616066.png")
> system("convert tmp/2ulic1258616066.ps tmp/2ulic1258616066.png")
> system("convert tmp/3g2y51258616066.ps tmp/3g2y51258616066.png")
> system("convert tmp/488tg1258616066.ps tmp/488tg1258616066.png")
> system("convert tmp/5oobi1258616066.ps tmp/5oobi1258616066.png")
> system("convert tmp/6xvap1258616066.ps tmp/6xvap1258616066.png")
> system("convert tmp/709fw1258616066.ps tmp/709fw1258616066.png")
> system("convert tmp/8xd1e1258616066.ps tmp/8xd1e1258616066.png")
> system("convert tmp/9qxiy1258616066.ps tmp/9qxiy1258616066.png")
> system("convert tmp/10h6001258616066.ps tmp/10h6001258616066.png")
>
>
> proc.time()
user system elapsed
2.392 1.530 3.634